In a continuous effort to improve control of longitudinal vehicle motion in very different terms it is essential to have to access on-line estimates of parameters as vehicle mass and road slope. Then, vehicle parameters variation plays an even larger role in automated control of vehicles (cars but especially light/heavy commercial vehicles, trucks, tractor & trailers, buses and so on), i.e. light commercial vehicles generally exhibit larger variations in parameters such as vehicle mass (up to 100% differences between loaded and unloaded configurations). Furthermore all main proposed fuel saving evaluation techniques are dependent on the knowledge of how the road ahead will behave, e.g. modest road grades may prove to be quite a challenge for vehicles with low power-to-weight ratio such as commercial vehicles, and how vehicle mass changes real-time and influences CO2 emissions. These facts highlight the need for estimation of road slope and vehicle mass on a motor vehicle, in particular on Light Commercial Vehicle.
In literature, proposed mass and slope estimation approaches are model-based approaches, and this is mainly due to need to scale studied algorithms on different production vehicles with a modular re-design.
A vehicle longitudinal dynamics equation will now be formulated in general form to understand plant non-linearity and/or time variance complexity.
The equation of motion at front wheels when the driveline is fully engaged and all mechanical power from engine is passed to the wheels, has the general form:
                                                        m              at                        ·                          x              ¨                                =                                    m              ·              g              ·                              sin                ⁡                                  (                  α                  )                                                      +                          F              P                        -                          F              R                                      ⁢                                  ⁢                              m            at                    =                      m            +                                          J                wheels                                            R                2                                      +                                          η                ·                                  J                  motor                                                                              R                  2                                ·                                  τ                  t                  2                                ·                                  τ                  d                  2                                                                                        (        1        )            where m is total vehicle mass, Fp is propulsion force and FR is resistance force, g is gravitational acceleration, α is slope angle, R wheel is radius and mat is equivalent mass at propulsion wheels, so total mass with in addition inertial effects of wheels (Jwheels) and motor (Jmotor), through gearbox and differential.
In the equation, unknowns are vehicle mass (m) and road slope (sin (α)), where vehicle mass, in practice a model parameter, has very slow dynamics while road slope is a ‘true’ real-time physical quantity with a medium slow dynamics. Seen differential equation (1) is a classic non-linear, time-variant equation. The simultaneous estimation problem requires an Extended Kalman Filter (EKF) design, but the invention's goal is to realize an integrated estimator which is simplest, enough accurate, quite robust and inexpensive under computational complexity and hardware point of view.
The literature presents many algorithms for online estimation of mass and slope.
Historically, there have been proposed estimation algorithms for either mass or slope estimation only; for example, for vehicle mass, algorithms linked to sharp longitudinal accelerations and decelerations which excite vehicle's mass significantly, thereby making this mass easier to estimate.
For example, U.S. Pat. No. 5,482,359 proposes using sharp controlled accelerations and decelerations as part of an event-seeking mass estimation method. Similarly, U.S. Pat. No. 4,548,079 proposes estimating vehicle mass specifically during the sharp accelerations and decelerations introduced by gear shifting. U.S. Pat. No. 4,941,365 proposes a similar mass estimator that explicitly compensates for wheel inertia. Further extensions of the same approach are proposed in U.S. Pat. No. 5,490,063, U.S. Pat. No. 6,167,357, U.S. Pat. No. 6,438,510, U.S. Pat. No. 6,567,734 and US 2007/0038357. In particular, U.S. Pat. No. 6,438,510 discloses estimating vehicle mass and aerodynamic drag coefficient by recursive least squares (RLS), wherein samples of the signals of interest are buffered and their validity is assessed.
On the other hand, there have been proposed algorithms for estimation of the road slope independently from vehicle mass based on longitudinal acceleration model, for example in WO 03/40652, or based on Kalman filtering applied to same longitudinal model, for example in WO 03/016837, and further extensions of same approach for example in U.S. Pat. No. 7,269,494 inferring also vehicle speed sign.
As far as simultaneous estimation of mass and slope, Bae, H. S., Ryu, J., and Gerdes, J. C., 2001, “Road Grade and Vehicle Parameter Estimation for Longitudinal Control Using GPS”, Proceedings of the IEEE Intelligent Transportation Systems Conference, for instance, propose a recursive least squares estimator that utilizes longitudinal force, acceleration, and GPS-based road grade measurements to determine vehicle mass and aerodynamic drag.
In WO 03/016837 and in Lingman, A., and Schmidtbauer, B., 2002, “Road Slope and Vehicle Mass Estimation Using Kalman Filtering”, Vehicle System Dynamics, 37, pp. 12-23, Lingman and Schmidtbauer investigate the possibility by Kalman filtering to estimate slope and mass using available information on propulsion and brake system characteristics, a vehicle speed measurement and the possible improvement through the addition of a longitudinal accelerometer.
U.S. Pat. No. 6,980,900 proposes again a recursive least squares estimator in which aerodynamic drag forces are simulated online and subtracted from force measurements, rather than estimated.
In Vahidi, A., Druzhinina, M., Stefanopoulou, A., and Peng, H., 2003, “Simultaneous Mass and Time-Varying Grade Estimation for Heavy-Duty Vehicles”, Proceedings of the American Control Conference, Denver, Colo., in Vahidi, A., Stefanopoulou, A., and Peng, H., 2003, “Experiments for Online Estimation of Heavy Vehicle's Mass and Time-Varying Road Grade”, Proceedings of the 2003 ASME International Mechanical Engineering Congress and Exposition, and in Vahidi, A., Stefanopoulou, A., and Peng, H., 2005, “Recursive Least Squares with Forgetting for Online Estimation of Vehicle Mass and Road Grade: Theory and Experiments”, Vehicle System Dynamics, 41(1), pp. 31-55, Vahidi et al. propose a similar estimator that does not require road grade measurements and estimates vehicle mass, drag, and road grade simultaneously using minimal instruments. The algorithm accommodates the time-varying nature of aerodynamic drag and road grade through multi-rate forgetting.
Winstead, V., and Kolmanovsky, I., 2005, “Estimation of Road Grade and Vehicle Mass via Model Predictive Control”, Proceedings of the IEEE Conference on Control Applications, propose an extended Kalman filter that estimates both longitudinal vehicle states and parameters (including mass) for adaptive cruise control.
EP 1935733 proposes a combined estimation in two steps: road slope estimation based on vehicle speed and longitudinal acceleration, and then on this base and drive train signals vehicle mass inference.
DE 10 2005 008658 discloses estimating vehicle mass based on engine torque and on vehicle speed and acceleration. In particular, vehicle mass is estimated based on the comparison of two different computation performed in different ways. Road slope is instead estimated based on the teaching in the above-referenced article of Lingman, A. and Schmidtbauer, B.
Finally, WO 03/023334 A1 discloses simultaneously estimating vehicle mass and road slope either recursively or using an extended Kalman filter or based on an RLS approach.